understanding human action: integrating meanings

Keestra – Interdisciplinary research of action understanding – final draft, April 2010 -1

Understanding human action: integrating

meanings, mechanisms, causes, and contexts.

(To appear in: Interdisciplinary research: Case studies of interdisciplinary understandings of complex problems; Repko, A. F., Newell, W. H., Szostak, R. (Eds), Sage Publishers (2011). Final draft (April 2010), do not circulate without permission.)

Machiel Keestra (University of Amsterdam)

Introduction

Humans are capable of understanding an incredible variety of actions

performed by other humans. Even though these range from primary biological actions

like eating and fleeing, to acts in parliament or in poetry, humans generally can make

sense of each other’s actions. Understanding other people’s action, called “action

understanding,” can transcend differences in race, gender, culture, age, and social and

historical circumstances. Action understanding is the cognitive capability of making

sense of another person’s action by integrating perceptual information about the

behavior with knowledge about the immediate and socio-cultural contexts of the

action and with one’s own experience.

Since it is necessary to integrate multiple sources of information, it is not

surprising that failures to understand a person’s behavior are also common. Well

known is the case of an autistic professor who compares herself to an “anthropologist

from Mars.” Incapable of spontaneously understanding why someone cries, she has

learned rules that help her to infer that people who rub their eyes while tears are

running down their cheeks are weeping and probably feel unhappy (Sacks, 1995). By

contrast, normal individuals automatically allow stereotypes, prejudices, self-interests

and the like to influence their understanding a person’s behavior (Bargh & Chartrand,

1999). More generally still, humans can easily misunderstand unfamiliar symbolic

Interdisciplinary work also helps to fight individual limitations. This chapter has benefitted greatly both in wording and thought from the meticulous help of the editors of the book, Allen Repko, Bill Newell and Rick Szostak. Needless to say that remaining errors still reflect my individual limitations.


actions or rituals if they rely too much on their own socio-cultural expertise

(Gadamer, 2004). Given the importance of action understanding in every domain of

human life and society, and in light of the complexities that surround it, a

comprehensive scientific understanding of this capacity is needed. Apart from

satisfying intellectual curiosity, such insight would serve to improve our action

understanding and mitigate several forms of misunderstanding. Indeed, in studying

action understanding, “we as scientists are engaged in the very process that is central

to our concerns” (Gergen & Semin, 1990, p. 1).

Scholars are increasingly dissatisfied with mono-disciplinary approaches to

understanding human action. Such one-sidedness can rest upon various motives. For

example, “hermeneutic interpretations” of action understanding tend to emphasize

historical and cultural influences while overlooking that ultimately such influences

depend upon individual cognitive processes.1 This has led to a critique of the

assumption that humans are born as a “blank slate” and that culture is solely

responsible for all cognitive contents. However, such critique easily slides into an

overemphasis on the biology of human nature and a denial of socio-cultural

influences on cognition (Pinker, 2003).

Fortunately, recent interdisciplinary endeavors have shown that an

interdisciplinary approach is preferable when investigating complex functions like

action understanding. Such research often involves developing a new “interdiscipline”

such as cultural psychology (Bruner, 1990) or combining insights from the social

sciences and psychology (Shore, 1996; Sperber, 1996). Evidence shows that

throughout human evolution there have been mutual influences between biological

and cognitive processes that shape human capacities and socio-cultural influences on

those processes (Bogdan, 2003; Donald, 1991; Tomasello, 1999). In addition to these

interdisciplinary investigations, computational sciences and artificial intelligence

research are developing computer models of human understanding that allow new

types of experiments and simulations (Churchland, 1995). Such insights underscore

the necessity and fruitfulness of disciplinary boundary crossing and require that

various disciplinary methods, concepts and theories be combined in innovative ways.

1 There is little room for evidence from the natural and social sciences in the hermeneutics as proposed in the influential (Gadamer, [1960] 2004). A theory of interpretation that allows scientific explanation a role in interpretation is proposed in (Ricoeur, [1986] 2008). In the social sciences an influential approach considers human functions as stemming from actor-network interactions, without specific interest in biological and psychological conditions (Bourdieu, [1980] 1990).


Presently, there is a need for a theoretical framework that is capable of

explaining a phenomenon as complex as human action. Such a framework requires

integrating insights from multiple disciplines. The purpose of this chapter is to

propose a “mechanism-based explanation”2 of action understanding which will

provide a theoretical framework for integrating various and often conflicting

disciplinary insights. Proposing an integrative theoretical frame is a common practice

in the sciences. Such a frame allows scientists to explain many facts that have been

observed, while predicting others. In the life and cognitive sciences, however, a

specific integrative device is often applied, namely “mechanism-based explanation.”3

As used here, “mechanism” means “an organized system of component parts and

component operations. The mechanism’s components and their organization produce

its behavior, thereby instantiating a phenomenon” (Bechtel, 2005).

In explaining action understanding, scientists assume that there is a complex

cognitive “mechanism” that is responsible for this phenomenon. Such a cognitive

“mechanism” can ‘produce’ action understanding as it processes multiple sources of

external information. Moreover, external influences can modulate or affect the

mechanism itself as is the case with socio-cultural information. For instance,

neuroimaging experiments show that the differences in family relations are correlated

with differences between the neural processes of Western and Chinese students when

they think about their mothers. In Western students, self-related thought activates

different processes than mother-related thought, while in Chinese students the two

processes are rather similar (Han & Northoff, 2008). Since action understanding

involves many more different sources of information, a mechanism-based explanatory

approach should be prepared for integrating insights from various disciplines.

2 Such an explanation is also called “mechanistic” in the philosophy of science literature (Machamer, Darden, & Craver, 2000). It is important to realize that in an explanatory context a mechanism is an epistemic device and plays a role in the organization of knowledge. If our knowledge of a phenomenon changes, the mechanism needs adjustments accordingly. 3 Since it proved to be extremely rare to demonstrate analogues of Newton’s mechanical laws for biological or cognitive systems, an alternative scientific device is considered more apt for these fields (Bechtel & Richardson, 1993; Machamer, Darden, & Craver, 2000). Meanwhile, social scientists are discussing the fruitfulness of a mechanism-based approach as well – see (Hedstrom & Swedberg, 1996), for example.

Mechanism-based Explanation in Brief

A simple and familiar example of a mechanism is a clock with components

like gears and shafts, and operations like turning and oscillating. If made properly and

provided with external inputs such as energy and correct initial settings, the clock will

establish time accurately. However, we cannot identify the mechanism that makes the

clock work just by observing its external pattern of behavior. To do that requires

going inside the clock and investigating its various components and operations.

Complex mechanisms may be analyzed at various levels. The human body, for

example, is a far more complex mechanism than a clock and must be analyzed at

various levels—anatomical, physiological, or biochemical—to be fully understood.

Each of these levels refers to the hierarchy of the body’s organization, not to the

physical size of the parts that exist at each level. Given the many and often non-linear

interactions between, for example, chemical substances, organ functions, and socio-

cultural meanings, that together can produce specific hallucinations, biological

phenomena are very complex. Compared to the human body, a mechanical clock is

not complex: underneath the observable level of shafts and gears is the unobservable

level of molecules. Note that molecular differences between clocks made from steel

or from silver do not affect the way they establish time whereas changing molecules

in blood will affect human bodily functions. Biological and cognitive mechanisms are

also far more complex than engineered mechanisms because of the non-linearity of

many intrinsic activities and their responsiveness to environmental factors, including

the meanings of socio-cultural settings and symbols.

Two strategies are used to develop a mechanism-based explanation of a

phenomenon: decomposition and localization (Bechtel & Richardson, 1993).

Decomposition means that we first analyze a given phenomenon—be it establishing

time or action understanding—into components or smaller tasks that in concert are

responsible for it. Localization means that we try to localize these components of the

phenomenon somewhere in the object or organism that displays it. In easy cases we

can localize the components of our phenomenon, like pointing the hours or the

minutes, in separate component parts and activities of the clock. However, these parts

and activities are not completely separable since they rely on the same energy source

and initial settings and share many other parts and activities. Typically, therefore, our



research leads to increasing specification and revision of the decomposition and

localization of the phenomenon with which we started. For readers who may be

unfamiliar with this approach, some clarifications are in order.

The first is that a mechanism-based explanation is not a complete description

of a clock, an animal or a brain. Rather, it is an explanation of a specific phenomenon,

event or behavior that is produced by the organized interaction of components and

operations. A mechanism-based explanation of action understanding as performed by

the brain will therefore contribute only in a limited sense to explanations of other

functions of the brain. Since a mechanism-based explanation could be given for each

function and for its components, a complete description would consist of an

unmanageable multitude of mechanisms, many of which would overlap and modulate

each other. Fortunately, explaining a specific phenomenon does not require this.

The second clarification is that a phenomenon may appear singular and

opaque, but if we are to give a (mechanism-based) explanation of it we must establish

that it is produced by different components and operations. Cognitive operations are

often called computations. These can be very simple, like addition, or more complex,

like face recognition. Figure 1.1 (from (Craver, 2007)) shows a schema of a

phenomenon, the activity of SΨ-ing.4 It also shows components X 1-4 that by

interacting in response to an external input produce the phenomenon (shown by the

four ovals below the solid surface). The arrows indicate the interactions that connect

the components, consisting mostly of simple activation or inhibition signals. These

interactions often include feed-back and feed-forward interactions between the

components and their operations. However, note that merely observing the

phenomenon (from the top down) does not reveal the complex mechanism and its

operation that produce the phenomenon. What appears on the surface to be a single

phenomenon is, in fact, a “distributed network” of smaller actions. Note also that the

phenomenon receives input (left-side arrow) and produces output, as do the

components and operations at a lower level. Explaining action understanding means

that we examine the cognitive processes that the human brain performs at various

levels and that together form the person’s capacity to understand the action or

behavior of another person.

4 If the phenomenon is complex, it is useful to de-compose such a phenomenon in sub-tasks, as we will do with action understanding. At the end of the chapter you can find a figure of this phenomenon.

Fig. 1. A phenomenon and its mechanism (from Craver, 2007, p. 7).

The third clarification is that such a mechanism can be a multi-level system

and can accordingly be examined at different levels. Obviously, we can study action

understanding while remaining at the personal level, where we observe which types of

action a person can and cannot understand, and examine the conditions that influence

his action understanding. Going into the brain to a first sub-personal level, we can

investigate which neural networks must cooperate to perform this function

appropriately. Going down to a second level, we can investigate isolated components

and activities in a particular neural area: its neurons, their interactions, and their

connections to neurons in other locations. If we need to be even more specific about

these neuronal activities, we can focus at a third level and describe the neurochemical

activities by which neurons pass on information to each other. Going in the other

direction, we can also climb one level upwards and consider the person as a

component: that is, consider him or her as a member of various social groups. At that

level, we are interested in the interactions between individuals and how they influence

each other’s action understanding, for example. For reasons discussed later, we don’t

need always to descend or ascend many levels when we explain a phenomenon like

action understanding. The study of neurochemical interactions at the third level may

still be relevant, but it is implausible that going as deep as the quantum mechanic

level of the human brain yields useful insights into action understanding.

The fourth clarification, and one that is particularly relevant to all cognitive

processes, is that mechanisms do not operate in complete isolation but are responsive


to various factors, including contextual factors. Organisms are open to external

information via their senses, but not always equally so because their motivation state,

attention and other internal processes influence this openness. Thus, the mechanism

governing action understanding will be influenced by a host of contextual variables.

The fifth is that organismic mechanisms are much more flexible systems than

other mechanisms. In organisms, we may observe that over time and due to learning

or development or to injuries, mechanisms responsible for a particular behavior have

changed or have been adapted—something a clock cannot do. Strikingly, an organism

may even develop different ways to produce the same behavior or phenomenon.

Automatization of a skill leads, for instance, to diminished involvement of conscious

control of movements, making it possible to perform other cognitive tasks

simultaneously. This can be made visible with the help of brain imaging techniques,

where experts and novices in a particular skill display strikingly different brain

activation patterns when performing similar tasks (Poldrack, et al., 2005).

A mechanism-based explanatory approach then, is particularly useful to

interdisciplinarians because it allows them achieve a more comprehensive

understanding of phenomena such as action understanding. In applying this approach,

interdisciplinarians can connect the mono-disciplinary insights in specific components

and operations at multiple levels and their intricate interactions that contribute to

human action understanding. How this works is the subject of the next section.

Part A: Drawing on disciplinary insights

Generally speaking, scientific efforts allow us ways to represent reality and

intervene in it (Hacking, 1983). Scientists represent reality by using mathematical

formulas, graphs, charts, mechanism-based and verbal explanations and the like.

Scientists intervene to test the adequacy of their representation or the predictions they

derive from it. Depending on the discipline and the representations, such interventions

range from digging for fossils in geological strata, sending particles through a

cyclotron, subjecting people to experimental conditions and adjusting variables in

computational programs used for simulations of phenomena. Choosing an adequate

type of representation of our insight into a phenomenon is an important matter, as is

the choice of an appropriate intervention to test it.


Engaging in interdisciplinary research is an even more demanding process. A

mechanism-based explanation allows us to assign disciplinary insights more or less to

particular levels: neuroscientists will focus on the neuronal and neural level,

psychologists at the higher level of action understanding and its components, while

sociologists will focus on the interactions between individual persons which influence

the properties of this understanding. The challenge for interdisciplinary integration is

to demonstrate how the components and activities that occur at different levels

interact with those at other levels. However, first we must decide which disciplines

are relevant for explaining action understanding. Relevance is thus a key term for this

first part of the interdisciplinary process, if only to keep it manageable.

1. Define the problem or state the focus question: decomposition

of action understanding

Action understanding is the subject of many disciplines. This is partly the

result of it being such a general and wide-ranging phenomenon with many different

properties. While acknowledging its variability, it is useful to formulate a general

definition. In what follows, we will consider action understanding as the result of

cognitive processes that an individual -- partly unconsciously -- performs when

making sense of another person’s actions. In doing so, one person has not only to

recognize the other person is acting, but also to include various sources of information

to interpret that action. As a result, the action can be understood, and perhaps

responded to appropriately .

By putting cognitive processes at the heart of the definition we will focus on

the cognitive information processing that goes on in the brain, for which we will

establish a mechanism-based explanation. This decision is in line with recent

developments in both the cognitive and social sciences. Indeed, we may even speak of

a “cognitive turn” in many disciplines. For instance, anthropologist Bradd Shore

argues for a “cognitive view of culture” (Shore, 1996, p. 39), concurring with his

fellow social scientist Sperber who argues for combining psychology with the study

of culture because of our “psychological susceptibility to culture” (Sperber, 1996, p.

57, italics in original). In accordance with that susceptibility, another author analyzes

the human mind as a ‘neurohermeneutic system’, for which “‘[i]nterpretation’ is the



operation of neurons to represent, and act upon, reality” (Reyna, 2002, p. 112).

Finally and more extreme is the argument that even “philosophical theories are

largely the product of the hidden hand of the cognitive unconscious” (Lakoff &

Johnson, 1999, p. 14). Note that putting cognition at the heart of these approaches

does not imply that there is no room left for external and socio-cultural influences on

action understanding. Nor does it imply that the culture-specific meaning of words

and symbols don’t matter. They do, and the study of cultural determination of

meaning is highly relevant. However, for such socio-cultural aspects to have an

influence on action understanding, they must exert this influence by affecting

cognitive processes that go on in the brains of individual persons.

Having defined action understanding and put cognition at its center, we now

have to take an important step. I mentioned in the previous section that we must apply

two heuristics, decomposition and localization. Ergo, we should first try to

decompose action understanding into smaller (sub-)phenomena that can be studied

more or less separately and subsequently integrate the results. Associated with that

decomposition is our localization effort that involves finding responsible cognitive or

brain processes that do the work. In fact, we have already localized action

understanding very broadly in the individual’s cognitive processes.

After having defined action understanding as a cognitive process, we can now

decompose it further by classifying it according to its contents.5 We can follow the

lead of hermeneutic philosopher Paul Ricoeur, who has devoted much of his work to

the theory and method of interpretation of human narrative and human action. Ricoeur

pointed out that we can approach action with a set of interrelated questions, focusing

on respectively: who, what, why, how, where, when?6 His analysis prioritizes three of

those questions, which offer three different perspectives on action: ‘what’ is the

action, ‘why’ is the action being done, and ‘who’ is the agent? Since ‘who’ the agent

is refers to the agent’s identity, social roles, continuous maturation and the like, this

information will generally not be captured by the other perspectives. For that reason,

5 Even though it is very well known that classifications and taxonomies often need corrections, revisions, or additions, they are extremely helpful in delineating otherwise overwhelming domains (Dupre, 2001).5 In the case of human capacities like action understanding, our preliminary classification inevitably starts from the use of our common vocabulary. This does not imply that such words that we commonly apply hold up to scientific scrutiny. Indeed, although we may expect that concepts in the domain of human experience are more robust than concepts like ‘ether’ or ‘vital force’, we may need to revise the former, too (Machiel Keestra & Cowley, 2009). 6 This set of questions has also been proposed in the context of the classification of scientific theories in (Szostak, 2004).

Ricoeur deplores “[t]he use of ‘why?’ in the explanation of action […] became the

arbiter of the description of what counts as action” (Ricoeur, 1992, p. 61). It will

become clear that to identify the relevant contexts, we need to know more about the

agent ‘who’ performed the action. Ricoeur’s emphasis upon the ‘who’ of action does

not imply that contexts do not matter, but it is a consequence of the fact that contexts

have to make themselves felt via an individual’s cognitive processing.

So, following the lead offered by a hermeneutic analysis of understanding

actions, we can decompose action understanding into three different, but probably

interrelated, component tasks: understanding what an action is, understanding why an

action has been performed, and a more thoroughgoing understanding of the ‘who’

behind it. Evidence does indeed confirm the possibility of disentangling these three

components of action understanding that have to do with, respectively, action

recognition, intention understanding, and narrative understanding.

2 Justify using an interdisciplinary approach: action

understanding as a multi-level phenomenon

Since our mechanism-based explanation focuses on cognitive processes, we

must first appreciate that cognitive sciences are themselves plural and that the field is

interdisciplinary. This has been the case from the start, as one of its pioneers recalls:

“I argued that at least six disciplines were involved: psychology, linguistics,

neuroscience, computer science, anthropology and philosophy. I saw psychology,

linguistics and computer science as central, the other three as peripheral” (Miller,

2003, p. 143). Note that interdisciplinary endeavors may range from a mere

borrowing of concepts or tools to the establishment of a new inter-discipline with its

own discipline-like contents, structures and conventions (Klein, 1990). In our current

research, different types of interdisciplinarity will be involved simultaneously.

Somewhat simplifying, I mentioned earlier that there is a link between the

contribution of different disciplines and the different levels of a mechanism. This is

easier to see in non-organismic mechanisms, as they are not so complex nor flexible

in their organization. From cosmology via molecular physics to quantum physics, we

can distinguish different disciplines focusing on particular levels of mechanism. They


use their own tools and methods and formulate different theories and hypotheses.

Even though the differences between, for example, quantum mechanics, relativity

theory and classical mechanics are considerable, I believe that in the cognitive

sciences the characteristics of the different levels and the associated differences are

more wide-ranging. For connecting sociological and psychological observations,

brain images, neurochemical interactions and genetic factors, we need a wide variety

of conceptual and methodological tools, whereas in the physical sciences we can draw

on the assistance of mathematics.

Given this complexity and the interdisciplinary nature of our investigations,

decomposing action understanding into action recognition, intention understanding,

and narrative understanding will make our task more manageable. Our next step is to

localize these task components somewhere ‘in’ the individual or rather in that

person’s cognitive apparatus – foremost the brain. The interaction between cognitive

processes and their contexts may involve social scientific and humanistic

investigations. Applying our mechanism-based explanatory perspective to the

decomposed task of action understanding, we will once more approach it as a multi-

level phenomenon. Of course, we already did that when decomposing the task into

three components, but now we are heading for the brain and neural tissue. Indeed, we

must try to assign the components and operations of our phenomenon to concrete,

localizable, bodily or neural areas and activities. Generally, if we start from a

particular level, we can look both upwards and downwards. Looking downwards

“involves describing lower-level mechanisms for a higher-level phenomenon” where

these mechanisms are responsible for sub-tasks of the task or phenomenon (Craver,

2007, p. 257). Conversely, if we look upwards, we may be able to see our mechanism

interacting with other mechanisms that together realize a new phenomenon at this

higher level. For instance, our action understanding will, in interaction with

perception, motivation, and motor action, contribute to the agent’s response to another

person’s behavior. At that level, it can be considered a component alongside several

other components and operations. So in looking downwards we can treat action

understanding as an independent variable and detect changes in the associated

mechanisms. Or conversely, we can investigate the changes in action understanding

due to influences at a higher level, for example. Figure 2 at the end of this chapter

may illustrate these investigative approaches to the mechanism that we are

discovering in association with action understanding.



Interdisciplinary collaborations are involved in the investigations of such a

mechanism, bringing different intervention and observation techniques with them.

Researchers can experimentally intervene in the components or operations at a

particular level and try to detect the consequences at another level -- upwards or

downwards. In the cognitive sciences -- including neuroscience -- such interventions

can be distinguished generally as interference, stimulation or activation experiments

(Craver, 2007). Stimulation involves, for example, the presentation of a stimulus to a

subject and detecting correlated activation at lower levels, down to single neurons.

Interference implies disturbing the normal mechanism, for instance by electro

stimulation of neurons, with subsequent detection of behavioral differences at a

higher level. Apart from such “vertically” directed interventions, researchers can try

to influence the mechanism “horizontally.” By varying the stimuli, researchers can

observe at various levels whether different mechanisms are activated, displaying

different properties in cognitive processing. Activation experiments can also be

combined with brain imaging techniques, which allow researchers to observe the

associated lower level activities of the posited mechanism, its components and

operations. The colorful images of brain activation patterns published in great

numbers are the results of such activation experiments, for instance. Furthermore,

there is also the valuable assistance of comparative work performed by ethologists,

developmental psychologists, computer simulation scientists, philosophers and again

social scientists and humanists. The latter disciplines can help to investigate whether

action understanding relies on different mechanisms in subjects from collectivist

versus individualist societies, or whether there is a difference in focus during

processing perceptual information between religious and non-religious subjects, for

instance. Clearly, limiting the number of disciplines to those that are most relevant to

the problem at hand is crucial to make the interdisciplinary research process

manageable – even though empirical evidence can lead to the need to include an

originally excluded discipline.7

7 Climate change models only recently include ocean dynamics, after marine scientists proved that these were involved in climate change, for example.


3 Identify relevant disciplines to involve in the mechanism-based

approach

In general, deciding which disciplines are most relevant for any given research

project can be guided by three questions: “Does the discipline have a well-defined

perspective on the problem?”, “Has the discipline produced a body of research (i.e.

insights) on the problem of such significance that its published insights and

supporting evidence cannot be ignored?”, and “Has the discipline generated one or

more theories to explain the problem?” (Repko, 2008, pp. 169-170). Although action

understanding is instantiated by a multi-level mechanism, it is not surprising to find

that disciplines focused on very low levels—like quantum mechanics—have not

delivered useful insights to it. Fortunately, not all events at such low levels make

themselves felt at much higher levels in a relevant way. Quantum phenomena do

occur in every atom of the brain, but if they affect cognition or behavior they do so

only when they influence functioning of specific neural areas. A cosmological

phenomenon like sunspots may impact on human cognition only when it influences

earthly temperatures which have an impact on environmental conditions of humans,

which finally can affect human cognitive processes. However, it is rarely the case that

a single neuron seriously influences a cognitive process that involves many more

neurons, or that human cognition is directly and irrevocably influenced by

environmental temperature. Therefore and in accordance with the observation that

everywhere in the universe we find “local maxima of regularity and predictability”

(Wimsatt, 2007, p. 209), we can restrict our multi-level system investigations to the

nearest levels of our phenomenon.8 Even though interdisciplinarians must be critical

of the traditional division of labor among disciplines and keep an open eye to

contributions from unexpected disciplines, the fact that we can conceive of

connections between extremely divergent disciplines is never reason enough to

overlook differences in relevance and specificity.

Meanwhile, a first estimation of relevant disciplines for our research of action

8 It is also related to the intriguing phenomenon of ‘emergent properties’: properties that occur at a particular level and cannot be explained purely on the basis of our knowledge of lower level components and operations. We can now say that such emergent properties are likely to depend partly on the systemic interactions between components and operations at the particular level itself and even that higher level (“top-down”) contributions will often be involved, leaving little room for strictly reductionist explanations of higher level properties (Bechtel & Richardson, 1993).

understanding can be made. While our initial topic of action understanding implies

inclusion of the full range of the cognitive sciences, the social sciences, and the

humanities to account for all varieties of action understanding, our first delineation

and decomposition of it has made the research more manageable. Instead of one broad

phenomenon, we are now able to focus on three distinct components (action

recognition, intention understanding and narrative understanding), which will lead to

different questions, research methods and results, and theories.

Most straightforward is arguably the investigation of the first component,

action recognition, which is the capability of “parsing” or sequencing continuous

bodily behavior or movements into distinct actions. It is plausible both from a

developmental and an evolutionary perspective that this parsing capacity is present in

newborns and animals. Consequently, we may at first exclude the humanities and

even social sciences from investigations of this component of action understanding.

Again, it may turn out that later cognitive or speech developments affect the

mechanism that carries out action parsing, but our preliminary hypothesis is that

without these developments action parsing is still being performed. With the

exclusion of those sciences, there are still enough candidates for inclusion such as

neurophysiology, developmental psychology, biology, and information science.

For the second component of action understanding, intention understanding,

we may need to include social sciences and humanities in our investigations. Note

that we cannot straightaway exclude those sciences that assist to explain action

parsing. Parsing remains generally a precondition for understanding intention, and it

appears in some cases to contribute significantly to understanding the specific

intentions of actions. For frequently repeated actions, it has been argued that primates

derive the intentional structure of complex actions purely on statistical processing

(Byrne, 1999). However, it is implausible that such processing should suffice for all

instances of intention understanding. How about discovering the intentions of newly

observed actions, or of irregular and complex actions, involving tools? There is much

evidence of humans taking a so-called “intentional stance,” assigning to the observed

agent the possession of mental states such as beliefs, desires and reasons. This stance

is extremely useful for predicting future behavior of relatively autonomous agents

(Dennett, 1989). Experimental observations show that even young infants expect that

agents are aiming rationally at a particular goal and show surprise if their behavior

contradicts this expectation (Gergely, Nádasdy, Csibra, & Bíró, 1995). For explaining


these mental states, we need to draw in more scientific disciplines, because these

states involve other types of social information, often mediated by language, symbols,

and so on. Social sciences and humanities can systematically investigate the

interactions between such influences and action understanding. These influences are

even transmitted at lower levels of the mechanism, and are correlated with the

patterns of activity of so-called mirror neurons which have turned out to play an

important role in this domain. These mirror neurons were discovered some 20 years

ago and have surprising properties, because they respond both to perception and to

observation or imagination of actions. Interestingly, their activation depends partly on

prior experiences of the observers, even with socio-culturally specific information.

Understanding and imitating an action that is within their ‘vocabulary’ or actions is

consequently easier than if they observe it for the first time (Rizzolatti & Sinigaglia,

2008). For this component we can draw on philosophical analysis of intentional

action, on behavioral biology and psychology, on the cognitive sciences, and perhaps

on social scientific research that focuses on socio-cultural specific means and goals of

action.

In some instances of human action, goals, objects, and instruments are

perceptually visible, facilitating intention understanding. However, often the action is

not or incompletely visible, or the action is ambivalent or is temporally extended, or

the perceptually available information is sparse. Not surprisingly, therefore, our third

component of action understanding, narrative understanding, relies much more on

higher cognitive processes and the use of narrative structures. We employ language,

concepts, abstraction, temporal and causal relations and the like when developing

narrative structures. These are generally of an abstract nature and cannot be perceived

through the senses. With the help of such structures, “we comprehend other people’s

minds by creating a coherent narrative or story of their actions, organized around their

goals,” including the conditions, the agent’s plans and possible outcomes (Read &

Miller, 2005, p. 125). These coherent narratives are naturally based in part upon the

observable properties of agents and their actions, but also depend upon previous

expertise and knowledge of the observer, including relevant socio-cultural

information. While making use of the insights pertaining to intention understanding,

for this component we need also insights from the humanities and the social sciences

about the construction and use of narratives, and theories of meaning in speech and

behavior. Moreover, we may want to check for cognitive scientific explanations of


these phenomena as well, in order to explain why schizophrenics have difficulties

with delivering narratives, for instance.

In sum, simple explanations of action understanding are not to be expected.

Even with appropriate neural and cognitive functions, humans will face limitations in

their capability of understanding each other because of the variability due to socio-

cultural influences on individuals’ cognitive processes. Indeed, social cognitive

scientists or cognitive anthropologists argue that socio-cultural specific differences in

action understanding have “two distinct moments of birth, one public and

conventional and the other a subjective appropriation and integration of a

conventional form by a particular person. The links between public models and

personal knowledge are contingent relations” (Shore, 1996, p. 371). Such contingency

applies less strongly to action recognition, as such statistical and perceptual processes

will be more generally present in individuals from different cultures—even though for

socio-cultural actions we may need specific expertise to be able to parse complex

ritual actions. Given the number of disciplines listed above, interdisciplinary research

can benefit from prudently leaving out a discipline if it is not relevant to the specific

component or operation in which we are interested.

4 Conduct a literature search

Having exposed the scope and complexity of action understanding, it is apt to

conclude that it is not a research topic but rather a comprehensive field of

interdisciplinary research. However, with the help of the mechanism-based

explanatory approach, we are able to narrow the scope of our research adequately.

First, by decomposing the phenomenon we have helpfully delineated three component

tasks: action recognition, intention understanding and narrative understanding. Now,

we can try to limit ourselves to one of the three component tasks when we engage in

behavioral, ethological, developmental psychological, or other studies at the

phenomenon level. We can investigate the conditions and results of handling the task,

and subsequently explain what in fact the apparent task consists of. We can also

observe whether adults, infants and animals are doing comparably well, and whether

their results are correlated with their language capabilities. A more advanced subject

would be the relations between the component tasks. For instance, recognition of an



action is sometimes facilitated by understanding its intention. Such relations therefore

complicate the mechanism.

Second, after having observed the phenomenon “horizontally,” we may then

look at it as a multi-level mechanism and view it “vertically.” Earlier I noted that we

do not need to go very high or low in the investigations of associated levels. So no

quantum mechanics or cosmology, but neurology, perhaps neurophysiology, and

cognitive psychology should be our prime domains for the literature search.

Third, science in general and mechanism-based explanation in particular is

especially interested in behavior, changes, and modifications. If nothing ever happens

to a phenomenon, it is difficult to give a representation of its relevant mechanism.

When investigating such a mechanism researchers can induce changes by using the

inter-level experiments that make use of the interference, stimulation and activation

techniques referenced earlier. Such experiments are relevant to our research.

Literature that refers to exceptional cases or pathologies should be included with

caution because the flexibility of the brain hinders generalization from such cases to

normal cognitive processes. For instance, even with dysfunctional mirror neuron

systems, autistic patients may be able to reach some intention understanding.

Fourth, parallel to these studies and drawing heavily upon them, in the

cognitive sciences and elsewhere researchers increasingly use computational

simulations of a phenomenon. When focusing on a specific component or operation,

comparison with results from such simulations may be informative. For instance,

when a specific mechanism is simulated in a neural network program, it can also be

used for virtual –or in vitro– interference, stimulation, and activation experiments as

is carried out on living subjects. More extravagant still, when building and testing

humanoid robots, roboticists use the insights of action understanding research. Such

research may provide us with information regarding which cues facilitate humans to

understand robot actions. For instance, a form of eye contact and gaze following by a

humanoid robot seems to be a prerequisite for effective interaction by humans with

them.9

Fifth, the social sciences and humanities contribute foremost to the

“horizontal” investigation of action understanding itself. For instance, cross-cultural

research could deliver insights into differences in action understanding properties. It

9 See (Breazeal, 2004) for more on this.

is implausible that socio-cultural differences have a large impact on the cognitive

mechanism itself, even though there is evidence of the co-evolution of language and

cognition (Donald, 1991). It is plausible, nonetheless, that a singular complex and

dynamic organ as the brain can lead to highly divergent processing when it automates

and stores much of the expertise that it gathers under specific socio-cultural

conditions. Anyone who has observed a chess master, a musician or a hierograph

reader perform their exquisite skills may doubt whether they have the same brain and

use the same cognitive processes as we all do – but, yes, largely they do. Even if these

distinct capabilities are only the consequence of modulations of the mechanism by

such individual and external influences, the results are relevant enough to our inquiry.

Sixth, sometimes researchers have established a specific topic that appears to

be representative of the phenomenon under scrutiny. In the case of action

understanding, the study of imitation has turned out to be exemplary for it. Studying

imitation, we gain insights in “two relationships that are central to understanding

minds in general and human minds in particular: the relationship between perception

and action and the relationship between self and other” (Hurley & Chater, 2005, p.

48). Meanwhile, imitation has been studied in various animals, infants, adults, and

computer simulations. Such an example facilitates interdisciplinary research and

translation efforts enormously.

As it is only after a first acquaintance with the literature that you may be able

to decide about these matters, interdisciplinary research truly is “a decision-making

process that is heuristic, iterative, and reflexive” (Repko, 2008, p. 137).

5 Develop adequacy with respect to the relevant components,

operations and interactions of the mechanism

After identifying the relevant disciplines, we must develop adequacy in them.

Then we should be able to decide about their specific relevance, what kind of

knowledge and how much we need from each (Repko, 2008, pp. 189-190). In the case

of the component tasks of action understanding, the range of disciplines that are

involved differ, leading to narrow or wider—in the case of narrative understanding—

interdisciplinarity. This distinction reflects the methodological and conceptual


distance between the disciplines (Newell, 1998). Achieving adequacy in research that

involves wider interdisciplinarity and that leads to an integration of insights is

obviously more difficult.

Fortunately, it is possible to reach adequacy in the case of investigations of a

multi-level mechanism and its components and operations. This is due to the

aforementioned fact that in such a mechanism there are “local maxima of regularity

and predictability” (Wimsatt, 2007, p. 209) even though these maxima may

themselves be produced by complex mechanisms. The regular and predictable

properties of atoms, for instance, hide various underlying probabilistic quantum

mechanisms. Or, referring to the figure at the end of this text, investigations may

focus on the local maxima that are represented by particular components or operations

that are included in that figure without having to cover all the rest. As a consequence,

there are many theories that describe and explain quite specific properties of the

system, perhaps under specific conditions. Such ‘theoretical pluralism’ is common in

the life and cognitive sciences, granting each theory only a relative significance for its

domain (Beatty, 1997). Since we are interested in a particular phenomenon, action

understanding or one of its three component tasks, we are permitted or even obliged

to select those insights that contribute significantly to our understanding of that

phenomenon: its occurrence, the components and operations that instantiate it, the

conditions under which it occurs, modulating influences from other processes, and so

on. Clearly, these insights will be different in kind. Some will be based upon

observations of action understanding in humans, animals or even computer

simulations; others will refer to brain imaging results that suggest correlations

between specific components and operations involved in relevant cognitive processes,

for example.

Adequacy in our case must not imply presenting a complete mechanism-based

explanation that comprehensively predicts and explains action understanding under all

possible circumstances, as this would be extremely difficult. Instead, we have already

described how we can limit our research project to just a component or operation that

contributes to it. Having done so, we can subsequently aim first to develop a

mechanism sketch which explains how the phenomenon might be constituted. Such a

sketch leaves room for other sketches that offer different possible mechanisms for the

same phenomenon (Machamer, et al., 2000). Once we provide such a sketch—or

several sketches—starting from our preliminary definition, decomposition and


localization of action understanding, our investigations should allow us to gradually

fill in the details of our mechanism-based explanation. Adequacy then means that we

have included those insights that contribute specifically to the instantiation of our

research phenomenon, while leaving out others. Given the complexity and flexibility

of cognitive systems and the phenomena they produce, it is likely that future scientific

developments will have an impact on what insights need to be included. I will

illustrate these remarks pertaining to adequacy with an example of such a delineated

phenomenon.

We observed in the previous section that distinguishing “what” an action is, in

some cases delivers information on “why” it is performed as well. This suggests that

adequacy with respect to action recognition would also satisfy requirements for

adequate knowledge of disciplinary insights for intention understanding. Since it

turned out that animals and human adults and infants recognize the beginning and end

of an action by noting that body movements differ unexpectedly, changing in tempo

and direction (Baldwin, Baird, Saylor, & Clark, 2001), it seemed that adequacy would

be relatively easy to reach. After all, in this context achieving adequacy implies

gaining insight into a relatively simple perceptual mechanism, which performs

statistical processing. Moreover, the visual stimuli that appear to require processing

are only those that are associated with changes of tempo and direction. Consequently,

the number of components and operations that are involved in these cognitive

processes are limited. Consequently, the number of disciplines involved is limited,

and we are able to specify the insights that we need from them.

However, action recognition and intention understanding turned out not to be

two completely overlapping processes in many cases. Indeed, action recognition is not

always dependent upon perceptual processes alone, as it is often modulated by other

cognitive components. Conceptual knowledge of actions and specific task

requirements, like the command to focus attention on specific aspects of a movie,

enables subjects to recognize more reliably and faster the precise moments an action

begins and ends (Baldwin, et al., 2001; Hard, Lozano, & Tversky, 2006; Zacks,

Kumar, Abrams, & Mehta, 2009). Apparently, the action recognition mechanism can

be modulated by components and operations that subserve other cognitive processes.

Consequently, adequacy here requires additional knowledge of the mutual constraints

between the originally simple mechanism and the properties of such modulating

influences of other cognitive processes and the conditions under which these take


place.

This insight into the greater complexity of the action recognition mechanism

forces us to reconsider our striving for adequacy. At least it implies that we need to

refine or further specify the adequacy requirements with respect to disciplinary

insights. For instance, if we aim to keep the explanatory mechanism simple, we

probably need to refine more narrowly the action types that can be recognized solely

on the basis of this perceptual process of action recognition. Secondary to that, we

must investigate whether it is plausible that this process can function in isolation at

all. This seems to be the case in primate understanding and imitation of actions, where

allegedly supplementary understanding of aims, goals, and intentions of the agent is

generally not involved (Byrne & Russon, 1998). In humans this appears not a

plausible way to proceed because action recognition and intention understanding are

different yet more tightly connected phenomena in that case. Indeed, the former is

generally subserving the latter: “Such initial, ‘bottom-up’ parsing would provide

appropriate units on which to base the additional processing needed to achieve

ultimate understanding of the intentions at play. This type of low-level mechanism

thus seems likely to be a crucial prerequisite to infants’ developing understanding of

the intentions motivating others’ actions” (Baldwin, et al., 2001, p. 715). Adequacy

means in the case of human action recognition the investigation of whether observers

often rely upon previous socio-cultural knowledge or other cognitive processes to

recognize the borders of an action or between actions. If that is the case, the

explanatory mechanism for action recognition must be expanded, and our adequacy

requirements will be more severe, too.

Sometimes, an empirical finding suggests that adequacy is within reach. For

instance, the discovery of mirror neurons in the Macaque monkey motor system was

not just exciting, but appeared also to bring some relief to researchers of action

recognition and intention understanding. The peculiar activation of these neurons both

during action perception and during action performance suggested to many

researchers that action recognition and intention understanding could be adequately

explained at once by referring to these neurons. Even though these mirror neuron

systems are already more complex than the earlier proposed and purely perceptual

action parsing mechanism, they did appear at least to operate in isolation from higher

cognitive processes that involve speech. Indeed, they are still held to enable “that

modality of understanding which, prior to any form of conceptual and linguistic


mediation, gives substance to our experience of others” (Rizzolatti & Sinigaglia,

2008, p. 192). Still, the question remains: Was the promise fulfilled that adequacy

with respect to action recognition and intention understanding implied having insights

in the action parsing mechanism and mirror neuron systems only?

Unfortunately not, as it turned out that in human intention understanding, still

more is needed. Due to the complexity of our actions, humans cannot always

recognize action borders even with the additional help of these mirror neuron systems,

let alone the intentions of complex actions. Especially, intention understanding seems

to often rely on a “mentalizing” approach, which is the — silent and unconscious —

application of a “folk psychology” or “theory theory” that people use to explain or

understand “why” someone acts as he does. Such reasoning includes the use of

implicit psychological theories about (human) actions, goals, reasons, desires and the

like (Stich & Nichols, 1993). Even though this process does not yet involve explicit

and conscious verbalization, such an explanation of intention understanding does

involve many more cognitive processes than are produced by the action parsing

mechanism or the mirror neuron systems alone.

Not uncommonly, as soon as we include higher cognitive processes that

involve language or reasoning in the explanatory mechanism, adequacy will be

increasingly difficult to achieve. For instance, the “theory theory” account of intention

understanding has rival theories. One of these is a “simulation” theory which suggests

that subjects implicitly project themselves into the place of the agent, when observing

an action. The simulation theory claims to be supported by mirror neuron research

because these neurons allegedly enable such silent and immediate simulation (Gallese

& Goldman, 1998). The narrative approach proposes yet another and different take on

action recognition and intention understanding in humans. It refers to the fact that in

most cases, we ask agents themselves “why” they did “what” they did. It is then

“these second-person deliveries—the narratives narrated—that do the heavy lifting in

enabling us to understand and make sense of others with confidence” (Hutto, 2007, p.

21). Ricoeur—who taught us the distinction between the “what,” “why” and “who” of

action—explains that a narrative in fact establishes a sort of “plot” around an action,

which includes the character of the agent, the events experienced and those acted on.

Together, these allow us to establish the identity of agents and their actions alike,

even if such a narrative will never be complete or definitive (Ricoeur, 1992). Such

contributions of narrative to the explanation of our capacity of action understanding—


and of acting itself—has only quite recently gained the interest of philosophers and

scientists (cf. (Bayne & Pacherie, 2007; Gallagher & Hutto, 2008; Hutto, 2007;

Ricoeur, 1984; Zwaan, Taylor, & de Boer, in press, 2009).

Developing adequacy can thus take us in different directions. It can imply

revisiting the observations of the phenomenon itself, in order to find out whether we

can isolate a specific class of actions that can be recognized by a simple perceptual

mechanism alone. Or adequacy may require us to expand this mechanism with the

mirror neuron systems, still fencing out those cognitive processes that include speech.

Unfortunately, in humans this may still not yield adequacy as our narrative and

intention understanding depend mostly upon modulating factors that lie outside the

scope of these perceptual and mirror neurons systems. With respect to the disciplines

involved, adequacy requirements are enlarged because additional components and

operations need attention. On top of that, new disciplines—such as the social

sciences—need to be involved in order to reach adequacy. It is to be expected that in

our next step(s) we will feel the impact of this.

6 Analyze the phenomenon and evaluate each insight into it

As research is never simply the accumulation of factual knowledge, we now

need to analyze the problem from the perspective of each relevant discipline, and

evaluate each insight into it (Repko, 2008, p. 217). A mechanism-based explanation

allows us to respect the differences between disciplinary perspectives even though we

will assign disciplinary insights into a phenomenon only a limited role in the

comprehensive explanation. Disciplinary theories, methodologies and assumptions

may hold for the investigation of a component or operation of the phenomenon but

may have only limited relevance for the overall phenomenon.

Thinking of a phenomenon as the result of the interaction of a multi-level

organization of components and operations has the advantage that those components

and operations may be investigated separately. Of course, it is tempting to

disciplinary specialists to isolate their domain and to maintain that their domain of

study and the insights it delivers are sufficient to explain the phenomenon, leaving the

rest aside as irrelevant. In such a case, the assumption is that the phenomenon can be



explained with reference to a specific “module” which is relatively isolated and

independently responsible for all properties of the phenomenon.10 Even though I

argued earlier that there is relative autonomy of levels in a phenomenon, in organisms

there are many feed-back and feed-forward interactions at a particular level as well as

top-down and bottom-up interactions between levels that can influence such a

‘module’. We must remain on the alert for this possibility, indeed.

Analyzing and evaluating the disciplinary perspectives on the phenomenon of

action understanding within the mechanism-based explanatory approach is a

straightforward task. Starting from a mechanism sketch, mentioned in the previous

section, researchers will complete and describe the components, their operations and

indeed the interactions within the mechanism in ever more detail. This process

consists partly of deciding about the relevance of the contribution of different

components and operations, and the further arrangement of them. In our case, analysis

of action understanding was already part of step 1, where we defined and decomposed

it into the three component tasks. Clearly, achieving adequacy and this task of

analyzing, evaluating and arranging insights are intimately related research tasks that

need to be carried out repeatedly during the interdisciplinary research process.

The interrelated research tasks of analysis and evaluation as applied to the

present case of mirror neuron research can demonstrate that the tight connection

between the definition, decomposition and localization of a phenomenon, the

experimental set-up used in investigating it, and the subsequent results require much

care. An inadequate definition will hamper research as much as a bad experiment.

Take for instance the following conclusion drawn on the basis of a mirror neuron

activation experiment: “to ascribe an intention is to infer a forthcoming new goal, and

this is an operation that the motor system does automatically” (Iacoboni, Molnar-

Szakacs, Gallese, Buccino, & Mazziotta, 2005, p. 533). This sweeping conclusion

was drawn on the basis of an experiment in which humans were looking at images in

10 A famous example of such an assumption pertains to language, which has been claimed to rest in a specific mental organ, separate from the other mental organs and therefore also localizable in specific parts of the brain. These claims are highly implausible, if we take our earlier analysis of mechanism-based explanations for functions in complex and dynamic biological systems into account. And indeed, former language modularity proponent Chomsky, wrote in an influential review in 2002: “a neuroscientist might ask: What components of the human nervous system are recruited in the use of language in its broadest sense? Because any aspect of cognition appears to be, at least in principle, accessible to language, the broadest answer to this question is, probably, “most of it.”” (Hauser, Chomsky, & Fitch, 2002, p. 1570). In that review, the authors engage in a more modest approach, similar to the one I present here for action understanding.

which a hand would take a cup for drinking or for cleaning, and in which a breakfast

table was shown. Sure enough, the results were interesting as they suggested that our

brain automatically includes context information in its prediction of the likeliness of

the subsequent action with the cup. Nevertheless, the authors overstate the relevance

of their results by defining intention extremely narrowly, while applying a very broad

interpretation of the operation of the mirror neurons in this highly suggestive and

restrictive task. This exaggeration is partly due to the lack of a comprehensive,

interdisciplinary analysis of the phenomenon of intention understanding and

consequently the lack of a rigorous evaluation of the results. As a result, the

researchers suggest that these mirror neurons function as a module which in isolation

fulfills the difficult task of intention understanding. Indeed, one of these investigators

still maintains that these mirror neurons “embody the deepest way in which we stand

in relation to each other and understand each other” (Iacoboni, 2008). However, in

human relations we often experience our mutual deep involvement and attachment

through verbal meanings, which will not always activate mirror neuron systems. Such

meanings may heighten our sensitivity to some actions over others. In sum, although

we know that the evidence is convincing enough to include mirror neurons in the

action understanding mechanism, we still need other components to account for

intention understanding.

Such overstated claims are more common than one would expect. Research of

human social action, for example, can be characterized as being mostly divided along

two opposed theoretical positions, subscribing to the idea of ‘Plastic Man’ or

‘Autonomous Man’: “Whereas Plastic Man, being formed by adaptive response to the

interplay of nature and nurture, is only spuriously individual, his rival is to be self-

caused. (…) Where Plastic Man has his causes, Autonomous Man has his reasons”

(Hollis, 1977, p. 12). Such extreme positions are often due to overestimation of

disciplinary strengths and underestimating disciplinary limitations with respect to a

complex and dynamic, multi-level system. Fortunately, interdisciplinary exchanges

may force researchers to integrate their insights in a much more complex, but at the

same time more comprehensive explanation.


B. Integrating insights

According to the model of the interdisciplinary research process that Repko presents

we would only now enter phase B, where the integration of insights takes place. Until

now, the focus was accordingly on “Drawing on disciplinary insights.” Even though I

have already looked ahead at the integration of these insights I will follow the

proposed research process and discuss the model’s next steps with respect to the

mechanism-based explanatory approach on offer here. Therefore, we need to identify

conflicts between insights and locate their sources (STEP 7) and then create common

ground between these insights (i.e., discovering one or more latent commonalities

between them) (STEP 8). Using this common ground we should be able to integrate

as many of the insights as possible (STEP 9). Eventually, this should bring us to a

more comprehensive understanding of human action and a way to it (STEP 10). The

mechanism-based explanation will prove a very useful instrument in these steps.

7 Identify conflicts between insights and locate their sources

Even though it is often the case that “[t]he possible sources of conflict

between insights are concepts, assumptions and theories” (Repko, 2008, p. 250), all

forms of interdisciplinary integration are not dependent upon resolving the conflicts

stemming from these building blocks of disciplinary perspectives on the phenomenon.

In the mechanism-based explanatory approach, conflicts of different types can occur.

One source of conflict stems from the initial phase of defining and decomposing our

phenomenon. In the previous section, we came across researchers who overstated

their conclusion, based upon an oversimplified definition of intention ascription.

A second source of conflict, related to such definition and decomposition

mistakes, is assuming that the underlying mechanisms of different component tasks

are completely separate. For instance, even though we distinguished the three

component tasks — action recognition, understanding of intention and narrative

understanding — we noted that it is likely that these components are intimately

related, both functionally and in their neural implementation.


Different from such conceptual conflicts are those that arise from

misunderstandings of the internal arrangement of components and operations of the

mechanism. For instance, interactions between components or interactions have been

neglected or overlooked—as is the case when the component task of action

recognition would be forgotten as a prerequisite for intention understanding.

A fourth source of conflict is the underestimation of the role of specific

context features on the way an action is being cognitively processed. For instance,

only under favorable conditions –- without time pressure, for instance -- and with

adequate preparation are people able to suppress or rather overrule the power of

stereotypical modes of understanding other people (Kruglanski & Orehek, 2007).

Fifth, conflicts can arise from the failure to acknowledge and evaluate

correctly the alternative processing trajectories that may prevail in specific groups or

individuals when engaging in action understanding. For instance, it is still debated

whether or not autistic subjects, who have difficulties in spontaneous human action

understanding and often use specifically trained theories about human behavior, suffer

from disturbances in their mirror neuron systems, forcing them to rely on other

trajectories (Blakemore, Winston, & Frith, 2004).

A sixth source is a combination of the latter two sources of conflicts, as

external, socio-cultural information may have become entrenched in the explanatory

mechanism and cause observable and regular differences in action understanding

processing, as is the case with modulation of mirror neuron activity due to individual

experience with socio-cultural information (Keestra, 2008). In general, for

explanations of the variability of socio-cultural influences on individuals’ cognitive

processes, we need to draw on both cognitive and on social scientific insights (Shore,

1996).

This is not an exhaustive list of the possible sources and locations of conflicts

in mechanism-based explanations, but they are the most prominent. The list also

demonstrates that considering a phenomenon from a mechanism-based approach is

useful as a heuristic when reflecting on potential conceptual and theoretical failures

and conflicts. In our domain of action understanding, such conflicts have arisen

predominantly with respect to the component of intention understanding.

In particular, the rivalry between an explanation that is based upon tacit folk

psychological theorizing by the individual and an explanation that refers to the silent

simulation of the observed actor, has been quite fierce. Conflicts pertained to the


definition and decomposition of intention understanding, explanations of its

components’ tasks, the underlying mechanisms and their neural implementations in

the brain.

The opposition between these theorizing and simulation accounts was

modified somewhat as soon as tacit theorizing was no longer necessarily associated

with propositions, rules, psychological causal laws, and the like, because only then

were animals and infants equally able to understand intentions. So when connectionist

models were developed and computer tested with some success, theorizing accounts

seemed to gain the upper hand in the conflict (Stich & Nichols, 1993). The balance

shifted dramatically again, the moment the surprising evidence of mirror neurons

appeared. Simulation theorists immediately appreciated it as support for their position

and took it as evidence for a neural basis for their idea that we employ similar

structures to both actively engage in action and passively understand that action. This

was clearly stated in a seminal article co-authored by a neuroscientist and a simulation

theorist: “[t]hus M[irror]N[euron] activity seems to be nature’s way of getting the

observer into the same ‘mental shoes’ as the target—exactly what the conjectured

simulation heuristic aims to do” (Gallese & Goldman, 1998, pp. 497-498).

Extensive analysis of the history of this conflict between theorizing and

simulation accounts of intention understanding would show that the conflict

alternately received energy from theoretical arguments, from neuroscientific

evidence, from behavioral observations, and so on. As I note in the following

discussion, we can meanwhile observe that the two former opposing positions have

successfully been integrated into an overarching mechanism.

The mechanism-based approach allows us to analyze such a conflict and the

different sources from which it arises. Similarly, it can function as a heuristic device

that enables us to handle the conflict by employing its apparatus of phenomenon

analysis, components and operations, levels, and interactions of sorts. Most important

for interdisciplinary research is, of course, how the mechanism-based explanation can

be adapted in such a way that it can integrate those insights or properties that

appeared to be in conflict with each other, while both sides can present some evidence

in support. For that, we need to take the next step, where we are asked to create or

discover common ground.


8 Create or discover common ground via a mechanism

Interdisciplinary investigations of human action understanding aim at the

integration of insights into that phenomenon. For such integration to succeed, it is

crucial to build upon a common ground that allows the integration of heterogeneous

materials. In our case this common ground should have the form of an explanatory

mechanism. Language processing is unlikely to provide such a ground, for instance,

as it cannot play a central role in explanations of action understanding in animals and

young infants. Nonetheless, given the strong interaction between components of any

comprehensive explanatory mechanism of action understanding, the explanatory

mechanism that we choose as common ground will be affected by the components

and operations that we will add to it. As noted above, language affects many other

components of the explanatory mechanism, including action recognition.

In fact, for most interdisciplinarians who use a mechanism-based approach

finding common ground for our interdisciplinary understanding involves the

identification of the most promising mechanism-based explanation among those that

have already been proposed in the literature. It is this mechanism-based explanation

as a whole that furnishes common ground. Note that this implies that generally such

common ground is already “composed of knowledge that is distributed among or is

common to disciplines” (Repko, 2008, p. 273).

After having identified a plausible explanatory mechanism as common ground,

interdisciplinarians need to demonstrate how other relevant components and

operations are related to it. What is implied in this endeavor is the rejection of claims

that a specific explanatory mechanism independently produces the phenomenon.

Embracing a mechanism as common ground can mean that it loses the exclusive

explanatory force it previously had. We have observed that mirror neuron systems

were taken to present the prime candidate for a mechanism-based explanation of

action understanding and imitation alike. Thanks to their functional properties, these

mirror neuron systems have been held responsible for enabling relationships between

oneself and another and between action and perception (Hurley & Chater, 2005).

Even though this makes these mirror neuron systems workable as common ground,

the associated mechanisms have meanwhile been embedded in a more comprehensive

mechanism, limiting their role accordingly. It is worth quoting a recent meta-analysis


that argues why still other systems or sub-mechanisms must be added to an

mechanism-based explanation of intention understanding with a central role for

mirror neurons:

First, an inconsistent or anomalous movement might be outside the perceiver's repertoire of familiar movements, so the mirror system cannot be of help. To resolve this, inferences of higher-level goals (e.g., “why did the actress fall?”) or other attributes (e.g., “was she depressed?”) seem to be needed, which are outside the scope of the mirror system. Second, when the perceiver reflects on a high-level intention of an action, this might necessarily engage the mentalizing system. It is possible that the mirror system is recruited for automatic lower-level goal interpretation (…) and the mentalizing system for reflection on the higher-level goals (from task goals to more general intentions). (Van Overwalle & Baetens, 2009, p. 580)

Nonetheless, various mechanism-based explanations of action understanding

have been presented in which mirror neuron systems function as common ground.

These systems offer then a common ground to different mechanism sketches or

models. Authors are sometimes even updating their own earlier model such as by

introducing a new model of action recognition learning by macaque mirror neurons which addresses data on auditory input, a model for opportunistic planning of sequential behavior, and studies of how to embed a macaque-like mirror system in a larger ape-like or human-like circuit to support ‘simple imitation’ and then ‘complex imitation.’ (Arbib & Bonaiuto, 2008, p. 45)

In this case, the previous model for imitation and understanding, with mirror

neuron systems as common ground, was expanded with components and operations

that process speech and symbol use. With that expansion, the authors are able to

explain the differences between human and monkey capabilities while leaving the

mechanism-based explanation of several commonalities largely intact.

What can be derived from this example is that, if possible, newly discovered

insights should be integrated into an existing explanatory mechanism. Obviously,

proposing a completely new mechanism is a much more demanding task. The various

adjustments of an existing explanatory mechanism in order to integrate insights into a

mechanism-based explanation are the subject of our next section.


9 Integrate insights into a mechanism-based explanation

This chapter’s argument for a central role for mechanism-based explanation is

in accordance with the general observation that “At the heart of any interdisciplinary

integration lies an integrative device—for example, a metaphor, complex explanation,

or bridging concept—that brings together disciplinary insights” (Boix Mansilla,

Duraisingh, Wolfe, & Haynes, 2009, p. 344). I hope to have convincingly shown that

a mechanism-based explanation is a useful device for achieving interdisciplinary

integration. After having identified an appropriate explanatory mechanism as the

common ground for explaining our phenomenon, the remaining task is to build on this

when seeking integration of additional insights.

Disciplinary insights are in that case included as explanations of components

and operations of the mechanism, or as explanations of the interactions that take place

within the mechanism or between the mechanism and external factors. Given the

explanatory mechanism that we have identified as common ground, integrating

further insights will lead to refining or expanding it. Refining implies that we can

specify the mechanism of a sub-component or -operation or interaction of the

overarching explanatory mechanism. For instance, the action-perception interactions

that mirror neurons facilitate are affected both by action-specific experiences of the

subject and the task one is performing (Rizzolatti & Sinigaglia, 2008). Expanding the

mechanism involves an extension with a component, operation, or interaction which

has turned out to influence the mechanism in a relevant way. For instance,

investigations of socio-cultural influences on the cognitive processes that underlie

action understanding are relatively new and may lead to unexpected extensions: some

processes turn out not to be sensitive to these influences, others are strongly affected

by them (Han & Northoff, 2008).

Refining or expanding the explanatory mechanism with an additional

disciplinary insight requires a careful consideration of the place within the mechanism

where the addition could be localized. This is especially difficult in the case of a

complex and dynamic multi-level mechanism, where an addition is likely to have a

widespread impact on many components and operations. The figure below of the

explanatory mechanism of action understanding demonstrates the many choices

available for assigning a location to the additional insight. For instance, should we


consider a socio-cultural influence like religious belief as a ‘socio-cultural model’ that

functions as a specific ‘computation’ for narrative understanding, having only via that

route a modulatory influence on the perceptual mechanisms that produce action

recognition? Alternatively, we could investigate whether it is the imitation of

religious practices, implying mirror neuron system activations, that modulate

perceptual processes. So knowing that religion influences action understanding still

leaves undecided where and how this influence should be integrated into the

overarching explanatory mechanism. Similarly, we already mentioned that the action

understanding deficits in autistic patients may or may not be wide-ranging

consequences of disturbed mirror neuron systems (Blakemore, Winston, & Frith,

2004).

Integrating an additional insight by way of a refinement or expansion of the

explanatory mechanism that we chose as common ground therefore requires us to

specify one or more relations between previously unrelated components or processes.

The results of mirror neuron research have indeed forced researchers to reconsider

explanatory mechanisms for imitation, for action understanding, for empathy, for

language processing, and for action learning – to mention the most prominent

(Rizzolatti & Sinigaglia, 2008). Obviously, refining or expanding the mechanism-

based explanations of mirror neuron system activities will in that case require a

variety of techniques, depending on the specific interactions that the additional

component is involved in.

In this context, it may be useful to realize that Repko mentions four integrative

techniques that are commonly used for establishing common ground. These are: (1)

redefinition of concepts or assumptions to include or exclude phenomena; (2)

extension of concepts and assumptions or expansion of a theory to cover previously

uncovered phenomena, perhaps even beyond their original disciplinary domain; (3)

organization of previously unrelated concepts or assumptions into a relationship, and

finally (4) the transformation of opposing concepts or assumptions into variables of

an uncovered factor (cf. Repko, 2008, pp. 282-291, and the second edition of his

book). Obviously, common ground depends on establishing a relation between

previously unintegrated theories. According to the present approach it is advisable to

identify a given explanatory mechanism as common ground, and subsequently to

refine or expand this. Interestingly, the four techniques can in modified form also be

applied to these refinements or expansions as discussed below.


For instance, the integrative techniques of redefinition and organization are

fairly common practice in the life sciences. They are often applied in combination as

can be learnt again from mirror neuron research. Among the many consequences of

the discovery of mirror neurons was the insight that the

rigid divide between perceptive, motor, and cognitive processes is to a great extent artificial; not only does perception appear to be embedded in the dynamics of action, becoming much more composite than used to be thought in the past, but the acting brain is also and above all a brain that understands. (Rizzolatti & Sinigaglia, 2008, p. xi)

Such rigid divides are also at stake when the integration of speech requires various

refinements and expansions of the mechanism-based explanation of mirror neuron

properties, which was proposed as common ground. These refinements and

expansions involve a re-organization of components and operations, comparable to

the third integrative technique of ‘organization of previously unrelated concepts or

assumptions into a relationship.’ Traditionally, language and action have been treated

as separate phenomena. Now that researchers realize that mirror neurons contribute to

both these cognitive processes, they are better able to explain the subtle and

sometimes disturbing interactions of language and action. This requires, however, a

redefinition (technique 1) of some basic assumptions concerning the ‘modular’

processes that were taken to underlie language and action.

Obviously, even when common ground has been established, further integration

of insights into the preferred mechanism-based explanation demands careful

application of various integrative techniques. Also, prudence and modesty are needed

with respect to the scope of the theories or explanations that require integration.

Researchers must realize that it is most unlikely that it is possible to localize cognitive

functions in particular neurons or mechanisms. Talk of neurons that ‘see’, or ‘feel’, or

‘infer’, should therefore be taken to mean that these components or operations play a

specific and decisive role in the comprehensive mechanism that accounts for that

function—no more, and no less (Keestra & Cowley, 2009).

To conclude, a general consequence of the integration of insights should be

acknowledged. Even if the focus is not on the assumptions or concepts of the

associated theories, integration will have an impact on the scope of these theories.

Integration of two theories has the consequence that their scope will become

expanded or contracted: expanded in the case when integration demonstrates that a


mechanism has also an impact on an additional component or operation, contracted

when the converse is true and a mechanism turns out to depend on another

mechanism for its operation, for instance. In cases where the integrated theory has

wide-ranging impact on the complex explanatory mechanism, both theory expansion

or contraction will obtain, though with respect to different components, operations or

interactions and the theories pertaining to these.

10 Present a mechanism-based explanation of human action

understanding and test it

Often, interdisciplinary understanding of a problem will be obtained at the final

stage of the research process. In a mechanism-based approach to explanation this is

somewhat different. As many—if not most—phenomena in the natural, life and

cognitive sciences do not appear contingently but reflect the behavior of a complex

mechanism, there are often already interdisciplinary mechanism-based explanations

available of components or operations of the eventual, overarching explanatory

mechanism. Remember the definition of a mechanism mentioned earlier: “[A]

mechanism is an organized system of component parts and component operations.

The mechanism’s components and their organization produce its behavior, thereby

instantiating a phenomenon” (Bechtel, 2005). When employing the mechanism-based

approach, researchers are aware of the fact that their specific research of such a

phenomenon should allow integration into such a mechanism as pertaining to a

component or operation or one of its interactions.

How we represent the explanatory mechanism is dependent upon its aim.

Complex and dynamic mechanisms, with their reciprocal constraints of components

and operations and their feed-back and feed-forward streams are difficult to represent

as a picture. Developing a computer program that includes such components and

operations is a more feasible method. However, if our interest is in the neural areas

that are involved in the mechanism, we may settle for a neuroanatomical map with

designated cortical areas and some broad processing streams. Or we might focus on a

finer grain of single neurons, like mirror neurons, and present the fine distribution of

those in a particular area and their connections. Often, researchers present their


findings in a relatively abstract ‘boxology’ where boxes and arrows refer to

components and operations. In that case, we can add labels to these that refer broadly

to their neural implementations in specific cortical areas. In the figure below, I have

presented most of the relevant information on action understanding contained in this

chapter in a mixed form of a boxology and the mechanism figure found in the

introduction. Clearly, it is far from complete and much more information could be

added to it or integrated with it. Specification of the components and operations

would require additional layers and theoretical descriptions of those but these are

beyond the scope of this chapter.

Finally, it is easy to see that such a visual representation of the mechanism can

assist further research in many ways, perhaps better than a verbally formulated theory

would do. Let me stress that as an integrative device mechanism-based explanation

does not exclude formulation of verbal and mathematical theories. Such theories often

focus on specific components or operations as we find them in the mechanism. As we

noted above, cognitive scientific experiments involve activation, interference or

stimulation of components or operations. The consequences of those can subsequently

be detected as changes in components or operations at other levels, or indeed in the

behavior of the subject. With the help of a mechanism sketch such as the one below

we can more specifically engage in the formulation of hypotheses and thinking of

potential interventions in it. We can consider horizontal interactions and their

potential effects, or we can reflect on inter-level investigations, using the interference

and stimulation techniques mentioned earlier. Such tests should lead to refinements,

adjustments or perhaps even the rejection of the proposed mechanism sketch.


Cognition Phenomenon: task definition (level 0)

Behavioral/verbal response

Action understanding

Perception

Moti-vation

Action

InteractionsPerson C

Person A

Action perception

Person B

Proprioception

Task decomposition

Action recogni-tion

Intention understan-ding

Narrative understan-ding

Auditory (verbal) perception

Visual perception

Motion detec-tion

Motor simula-tion

Theorizing

Action configura-tion

Linguistic structures

Socio-cultural models

Causal knowledge

Action cognition

Prefrontal lobe areas

Mirror neuron areas

Synaptic signals

Speed dectec-tion

Compu- tations

Retinal image

Agent/ Figure detection

Cortical implementations (level -1)

Neuronal interactions (level -2)

Figure: a highly simplified and incomplete mechanism sketch of human action understanding. Many dynamical (feedforward & feedback, top-down & bottom-up) interactions are left out. Further components and operations, like attention, memory, and awareness could be added to the sketch as they modulate action understanding. Note that each component and interaction could in turn be investigated as a complex phenomenon on its own, requiring a mechanism-based explanation. Note as well, that components may play a role in other mechanisms and contribute to various phenomena simultaneously. This is the case with mirror neurons, for example. (© M. Keestra)


Concluding remarks

Action understanding is a complex cognitive capability performed by a complex

cognitive mechanism. As we learnt above, that mechanism is comprised of various

components and operations, that are themselves open to mechanism-based

explanations. In developing a mechanism-based explanation of action understanding,

we need therefore to integrate insights from various disciplines by attributing these to

components or operations or to specific interactions in which the mechanism

participates. Strikingly, although there are many non-linear processes involved and

notwithstanding the complexity of the overarching mechanism, the phenomenon of

action understanding displays relatively stable properties under specific conditions.

The method proposed in this chapter requires application of the heuristics of

definition, decomposition and localization. However, not all phenomena lend

themselves to this explanatory approach. If a phenomenon does not appear at all to

behave in an orderly fashion, or if it turns out to be impossible —even preliminarily—

to localize and identify components or operations, then we may have to look for

another approach. Fortunately for us, human action understanding is a phenomenon

suitable for this approach. By learning more about the complex and dynamic

mechanism that explains it, we can appreciate even more this crucial capability and

cope with its constraints and limitations. Indeed, rather than being satisfied with

explanations of its successes, it may be even more important in our global society to

acknowledge the fragility of our capability of understanding.


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